The risk-based approach (RBA) is a regulatory requirement in anti-money laundering (AML) compliance, but in practice, it is rarely as dynamic as it needs to be.
A new webinar from SymphonyAI, explores how firms can leverage agentic AI as the engine of continuous risk assessment and control calibration.
Across the industry, firms have largely implemented the RBA as a governance framework rather than a live operational capability. That distinction is becoming increasingly costly.
The threat landscape has shifted fundamentally. Financial crime risk is now continuous, global, and evolving at pace. Criminal networks are innovating faster than compliance control cycles can keep up, and the core challenge is no longer a lack of data. Rather, it is the ability to interpret risk intelligence quickly enough to keep controls properly aligned with the actual threat environment.
Regulators are taking note. The question being asked is no longer simply whether a firm has an RBA framework in place. Supervisors now expect institutions to demonstrate continuous risk alignment and the pressure to evidence this in real time is intensifying.
This is where agentic AI enters the picture, SymphonyAI said. Unlike conventional compliance tools, agentic AI is capable of continuously monitoring a wide range of rich risk intelligence sources, enabling a fundamentally new approach to compliance operations. Its applications span automated risk alignment, control calibration, typology identification and classification, customer risk profiling, and operations prioritisation, among others.
In the session, SymphonyAI AML, FinCrime and Sanctions subject matter expert Elizabeth Callan outlined how agentic AI can reshape the compliance function in three core ways. First, by automating the monitoring of risk intelligence. Second, by accelerating and enhancing the risk assessment process. Third, by dynamically aligning risk intelligence and assessment outputs directly to control calibration.
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